1. Shopper Traffic Case Study:
An Analysis of Three Drug Stores After the CountBOX
Traffic Solution Was Implemented

2. Goals and Objectives of the Analysis
Show exactly how the results of the study can be projected from the 3 drug stores to the entire chain.
Determine the growth points and the ROI for the stores

3. Sales Analysis not using Shopper Traffic
Тable 1: Transactions from June 2015
Store #12
Store #25
Store #63
Average Dollars per Transaction
515
461
354
Number of Transactions
11 042
9 913
4 647
Total Revenue 1 2 3
Main Goal: Determine Growth Potential Points
Solution: Analysis using Transactions and Average Dollars per Transacion
Initial Conclusion:
Store #12 has the best performance followed by store #25 and store #63. To improve, store #25 needs to
increase its dollars per transaction. Store #63 needs to increase both the number of transactions and the
average dollars per transaction. To attain this, the store needs to drive more traffic to it perhaps by doing
more local marketing.

4. Sales Analysis not using Shopper Traffic (continued)
Substituting the maximum values of the average dollars per transaction and number of transaction,
The maximum potential of every store is calculated:
Тable 2 Maximum Potenial
Store #12
Store #25
Store #63
Potential Average Dollars per Transaction
515
Potential Number of Transactions
11 042
9 913
Potential Revenue
Difference (Potential Revenue – Actual Revenue ):
Since we lack additional data such as store shopper traffic data, we cannot determine if store #25 or store 63 could reach their maximum revenue potential. We cannot determine if the stores are staffed correctly to their store traffic levels or whether the stores are converting a sufficient percentage of the shoppers that enter the stores.

5. Analysis Including Shopper Traffic Data
Three drug stores with traffic counting data:
Тable 3. Key Indicators June 2015
Store #12
Store #25
Store #63
Total
Shopper traffic
17 961
14 382
8 008
40 351
Conversion rate
61.48%
68.93%
58.03%
63%
Average dollars per sale
515
461
354
465
Number of transactions
11042
9913
4 647
25602
Revenue
Revenue per visitor
317
205
295
Goal: Find potential for growth
The first insight is that the number of transactions does not equal the stores’ shopper traffic. The average conversion rate is 63% across the three stores. For 37% of the shoppers, they entered the store but did not purchase. Increasing conversion rate is an area for growth potential.

6. Analysis Including Shopper Traffic Data (continued)
Initial conclusion 2:
Store #12 can increase its conversion rate
Store #25 should increase its average dollars per sale
Store #63 should increase both its conversion rate and its average dollars per sale
Since store #25 has the highest conversion, an increase in its marketing budget may increase sales if the store could hold its conversion rate. Since store #63 has the lowest conversion rate, it may need to increase the customer experience to promote more sales or have sales people be more attentive to customers to help increase conversion rate.
Table 4. Key Indicators June 2015
Store #12
Store #25
Store #63
Shopper Traffic
17 961
14 382
8 008
Conversion Rate
61.48%
68.93%
58.03%
Average Dollars per Sale
515
461
354

7. Analysis Including Shopper Traffic Data (continued)
Substituting the maximum values of conversion and average dollars per sale, while leaving the stores shopper traffic, we obtain the potential of each
Table 5. Potential calculation
Store #12
Store #25
Store #63
Total
Shopper traffic - actual
17 961
14 382
8 008
40 351
Potential conversion rate (maximum)
68.93%
Potential average dollars per sale (maximum)
515
Potential revenue
Growth potential (Potential revenue – actual revenue)
The growth potential goals may be hared to achieve but they are realistic. Based on the potential of the stores in terms of shopper traffic, the stores with the most potential are store #12 and store #63. However, all three stores have to work to improve performance to reach their full growth potential.

8. The Main Question for the Retail Chain
Is the observed conversion rate of
63%
the norm for the chain and is there potential to grow conversion further?
Considering that if the conversion rate grows by 1% point, sales revenue will grow by 1.55%. This leads to an increase of $220,000 per month.

9. What Impacts Conversion Rates?
Revenue
Shopper Traffic - store
Regular customers
New customers
Traffic due to location
Traffic due to marketing
Average Ticket
Conversion Rate
Pricing Model
Pricing consistent with market
Merchandising
Breadth of merchandise
Depth of merchandise
Employee Training
Product knowledge
Customer Service Level
Wait time
Shoppers per employee
Ability to service customers consistently
The conversion rate reflects the customer experience across the whole chain. The higher the conversion then the better the chain is capitalizing on its opportunity.
In the three store pilot, the conversion rate is not consistent across the stores. Thus the stores’ customers are underserved and there is an opportunity for growth.

10. Analyzing Productivity on a Store Level
Formula:
Organic store growth =
Shopper Traffic
X Conversion Rate
X Average Dollars per Transaction
Blue = traffic Yellow = Avg. Ticket Orange = Conversion Rate

11. Analyzing Productivity on a Store Level (continued)
Store #12
Store #25
Store #63
Traffic
Conv. Rate
Avg. Ticket
Avg. Tocket M
622
68,49%
456,2
569
71,88%
439,5
245
74,69%
337,4 Tu
639
59,78%
536,0
564
71,81%
469,8
286
71,33%
342,2 W
651
58,86%
498,6
488
74,18%
400,5
226
76,11%
429,2 Th
590
63,93%
522,7
498
68,27%
467,8
258
66,67%
357,2 F
610
67,50%
553,5
514
71,79%
453,9
223
78,48%
349,5 Sa
543
62,58%
527,6
392
72,45%
399,3
175
81,71%
303,6 Su
480
64,95%
550,6
345
73,04%
372,0
162
75,93%
300,8
682
59,09%
504,3
536
69,22%
283
70,32%
306,0
619
65,30%
517,6
524
63,89%
412,1
255
70,20%
346,6
650
61,85%
482,7
504
69,98%
451,8
249
67,96%
335,5
554
73,47%
609,5
462
69,86%
528,1
240
78,75%
431,0
449
76,17%
532,4
505
54,26%
511,1
247
55,47%
366,1
525
60,00%
493,1
402
65,92%
405,7
219
48,33%
359,9
485
57,11%
520,1
377
71,68%
466,8
46,47%
362,1
649
62,25%
519,1
547
71,80%
481,5
329
55,02%
410,4
549
67,76%
534,8
52,07%
348
41,71%
419,0
675
61,63%
535,5
500
69,55%
501,8
243
74,49%
414,0
528
503,3
407
79,61%
505,1
196
88,27%
359,7
672
57,44%
558,2
493
69,57%
478,8
224
64,29%
294,9
604
58,94%
460,7
437
73,91%
399,6
129
90,70%
449,6
55,68%
526,5
389
70,44%
469,7
117
94,87%
391,5
715
57,06%
510,3
577
68,80%
443,8
174
86,78%
360,6
585
56,58%
475,1
481
68,19%
263
60,08%
337,2
627
60,29%
582,5
64,19%
494,2
277
60,65%
315,1
687
59,39%
447,9
482
76,76%
474,8
266
65,04%
311,1
710
58,17%
534,5
508
66,34%
534,2
294
54,08%
271,7
501
58,48%
444,7
72,41%
428,0
354
32,77%
437,3
428
64,72%
590,4
396
67,68%
408,3
457
24,73%
278,6
51,03%
456,3
529
66,16%
470,7
530
26,98%
299,9
722
58,73%
472,1
68,75%
497,2
494
30,16%
344,9
Month
17 961
61,48%
515,0
14 382
68,93%
460,6
8 008
58,03%
353,8
Weekend
14 294
61,95%
518,1
11 663
68,35%
470,5
6 606
57,78%
350,4
Work day
3 667
59,65%
502,8
2 719
71,40%
420,0
1 402
59,22%
369,4
Red highlight – Days when the conversion rate was significantly lower than the monthly averages. Further analysis is required to determine why the conversion rates were lower on these days such as did all employees report for work, was staffing done correctly, was product available or did competitors have sales.
Yellow highlight – Notes that conversion rates are lower on the weekends within the 3 month pilot. More analysis is needed to determine the cause such as whether there were stock-outs or employees did not close sales properly.

12. Analyzing Productivity on a Store Level (continued)
Stores #25 & #63 – Show declining conversion rates over the 4 week period. This is not a seasonal trend and intervention is needed to stop the decline
Weekly report on Key Indicators
Store #12
Store #25
Store #63
Traffic
Conv.
Avg. Ticket
Week 1
4 135
63.65%
519
3 370
71.84%
433
1 575
74.41%
348
Week 2
4 195
62.19%
527
3 337
71.41%
443
1 613
73.65%
343
Week 3
4 175
63.02%
524
3 297
70.15%
434
1 582
73.51%
344
Week 4
4 174
63.48%
522
3 314
69.53%
442
1 605
72.29%
330
Store #12 – Had a decline in conversion rate during week 2 when it had a simultaneous increase in traffic. The store lost sales probably because it was not staffed properly to meet the addition influx of customers

13. Analyzing Productivity on a Store Level (continued)
Week over week percent changes
Store #12
Store #25
Store #63
Traffic
Conv.
Avg. Ticket
Week 2
1.45%
-2.29%
1,54%
-0.98%
-0.60%
2.31%
2.41%
-1.02%
-1.55%
Week 3
-0.48%
1.33%
-0,52%
-1.19%
-1.77%
-2.07%
-1.92%
-0.19%
0.20%
Week 4
-0.02%
0.74%
-0,50%
0.50%
-0.88%
1.84%
1.43%
-1.67%
-4.03%
Usually, traffic and conversion rate move in opposite directions. That is, when traffic increases, conversion usually falls. This is the case in week 2 for Store #12. Unfortunately, in the cases of Stores #25 and #63, there are weeks where traffic decreases and conversion decreases. Management should monitor the situation in each store and take corrective actions as needed to keep the conversion rate stable and to avoid losing sales.

14. Conclusions
In this pilot, traffic counters were installed and traffic-based Key Performance Indicators (KPI’s) were used to monitor the stores. The KPI’s included traffic, conversion rate and average ticket. These measures allow store management to assess the stores’ potential and to determine “growth” opportunities when making decisions to improve chain operations. These include:
Sales management (sales plan, growth points and evaluating productivity)
Reorganization/optimization of the chain (opening/closing of stores)
Marketing (determining the effectiveness of marketing campaigns)
Property management (assessing lease arrangements for each location)
Growth potential (conversion rate of the stores and conversion rate by department within store)

15. Conclusions (continued)
At a high level, the equation for organic growth includes:
Shopper traffic
Conversion rate
Average ticket
These measures are the “standards” for proper retail store management.
The indicators must be reviewed on a daily basis for controlling store operations.

16. Conclusions (continued)
Although the pilot included only three stores, insights were gained as to how much revenue growth could be attained across the whole chain.
The introduction of key KPI’s such as shopper traffic and conversion rate increased the quality of management decision making by focusing resources efficiently to the areas where attention was needed the most.

17. The Main Obstacles to Improve Effectiveness
Counters are installed but managers do not use the data.
Employees have their own assumptions on traffic and do not believe the counters.
Shopper traffic count data is not integrated into the day to day reporting system.
Key indicator data are not forwarded to the proper personnel.
C-level and other key managers do not pay attention or act on the data.
The reasons why these obstacles exist are:
Lack of a systematic approach when dealing with the indicators
Lack of motivation to act
Lack of training on how to act on the indicators

18. What should be done to launch a shopper counting solution successfully
Stage 0
Pre-project study of the cost of implementation
Pre-project research to implement the traffic-oriented KPI’s into the current business KPI’s
Calculate the ROI on the implementation
Stage 1
Install shopper traffic counter sensors
Setting up the reporting system and the training of employees to use the traffic oriented data and KPI’s
Stage 2
Implement the usage of conversion rate and average ticket in the KPI’s for the stores and the chain
Implement shopper traffic KPI’s in the marketing department
Start to use shopper traffic data for proper employee scheduling
Stage 3
Use conversion rate, average ticket and shopper traffic count data in the strategic planning process for the chain
Stage 4
Deployment of the traffic counting system and reporting across the chain